Solve Multi-area Economic Dispatch Research Articles

A reliable deterministic method for multi-area economic dispatch considering power losses

This paper offers a reliable deterministic method for solving multi-area economic dispatch (MAED). The proposed method can handle nonlinear power losses in electrical networks and discrete and nonlinear characteristics in power plants, such as fuel change facilities and nonconvex cost functions. To this end, the presented method reformulates the products of two variables in the power loss terms as one-dimensional expressions. Then, it converts all nonlinear terms in the MAED into linear segments. Moreover, it handles discrete decisions in fuel selection through binary variables. We assess the performance of the proposed method by some MAED test systems. The results show that the method can reliably obtain accurate solutions in the adopted test systems. It converges to costs of 10604.7, 654.7, 121592.7, 124628.4, and 169528.9 respectively in 2-area 4-unit, 3-area 10-unit, 4-area 40-unit, 2-area 40-unit, and 10-area 130-unit. These findings reproduce or outperform results found in earlier optimization algorithms. Moreover, the computational time evaluations indicate that the method can solve the adopted MAED problems in less than one minute, showing its efficiency. Overall, the obtained results confirm the efficacy of the method’s efficacy in handling the MAED with discrete and nonlinear terms.

Solving multi-area economic dispatch problem using hybrid exchange market algorithm with grasshopper optimization algorithm

Solving multi-area economic dispatch problem using hybrid exchange market algorithm with grasshopper optimization algorithm

Solving Multi-Area Economic Dispatch with Multiple Fuels Using Hybrid Optimization Technique (Dept. E )

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Fast Convergence Evolutionary Programming for Multi-area Economic Dispatch

This paper develops and suggests fast convergence evolutionary programming (FCEP) for solving multi-area economic dispatch problem with tie-line constraints, transmission losses, multiple fuels, valve point effect, and disallowed operating region. Evolutionary programming (EP), a class of evolutionary algorithm, is based on the basic genetic operation of human chromosomes. EP has the ability to seek out the global or close to the global optima. FCEP has been developed to boost convergence speed and solution quality. The efficacy of the developed technique has been tested on three different types of test systems. Test results have been compared with those acquired from improved fast evolutionary programming, EP and other stated evolutionary techniques. It has been observed from the comparison that the developed FCEP has the ability to offer superior solution.

Water Wave Optimization Algorithm for Solving Multi-Area Economic Dispatch Problem

Water Wave Optimization Algorithm for Solving Multi-Area Economic Dispatch Problem

Equal Incremental Fuel Cost Approach for Multi Area Operation of Power System

This paper presents a novel approach, Equal Incremental fuel cost (λ-Concept) approach, for solving Multi Area Economic Dispatch (MAED) problem. It is a simple approach and developed from the basic observation of incremental fuel cost of an area. The proposed approach has been tested on 4-area system with four generators in each area and a large 2-area system consists of 120 generators. The suggested algorithm has been tested extensively by considering the different tie line power transfer limits and useful recommendations are provided. Further, the impact of tie line power transfer limits on total fuel cost is also discussed. It is found from the test cases that the proposed method is shown to be robust, very fast and extensible to include a large class of problems. The simulation results of the proposed method have been compared with the existing methods.

Multi-Area Economic Dispatch Performance Using Swarm Intelligence Technique Considering Voltage Stability

Economic dispatch is one of the important issues in power system operation and planning. Economic dispatch requires reliable technique to achieve minimal cost; otherwise, a non-optimal solution may cause non-economic electricity generation to the utility. Single area economic dispatch does not make complete electricity generation of the whole system on the electrical transmission network. Thus, multi-area economic dispatch implementation leads to complete consideration for power transmission system. This paper presents multi-area economic dispatch performance using swarm intelligence technique. In this study, swarm intelligence technique, namely the particle swarm optimization technique (PSO) is employed for solving multi-area economic dispatch problems. The algorithm is tested on a 2-area 48-bus power system with different case studies. Variation in active power loading in achieving an optimal solution is also considered in this study. Several trials were taken into consideration to assess the consistency of results. Comparative studies were performed with respect to evolutionary programming (EP) and revealed that PSO yields better results as compared to the EP.

Fully distributed multi‐area economic dispatch method for active distribution networks

Distributed generators can be integrated in geographically distributed areas and microgrids of active distribution networks (ADNs), and can operate independently. On the basis of the alternating direction method of multipliers, the authors describe an efficient fully distributed algorithm to solve multi-area economic dispatch problems in ADNs without requiring a central coordinator. The physical interpretation of the distributed algorithm and a proof for its convergence are both given. Network losses are taken into account by exploiting the features of ADNs. Two other popular distributed algorithms, including Lagrangian relaxation and auxiliary problem principle, are also implemented and compared with numerical tests. They discuss numerical results that demonstrate the effectiveness and efficiency of the algorithm.

Multi-area Economic Dispatch Using Improved Particle Swarm Optimization

This paper presents improved PSO (IPSO) to solve Multi Area Economic Dispatch (MAED) problem. The objective of MAED problem is to determine the optimal value of power generation and interchange of power through tie-lines interconnecting areas in such a way that total fuel cost of thermal generating units of all areas is minimized while satisfying operational constraints. The control equation of the proposed PSO is modified by suggesting improved cognitive component of the particle's velocity by suggesting preceding experience. The operating parameters of the control equation are also modified to maintain a better balance between cognitive and social behavior of the swarm. The effectiveness of the proposed method has been tested on four areas, 40 generators test system. The application results show that IPSO is very promising to solve large-dimensional MAED problem.

Multi-area economic dispatch with reserve sharing using dynamically controlled particle swarm optimization

Abstract A dynamically controlled PSO (DCPSO) is proposed to solve Multi-area Economic Dispatch (MAED) problem with reserve sharing. The objective of MAED problem is to determine the optimal value of power generation and interchange of power through tie-lines interconnecting the areas in such a way that the total fuel cost of thermal generating units of all areas is minimized while satisfying operational and spinning reserve constraints. The control equation of the proposed PSO is augmented by introducing improved cognitive and social components of the particle’s velocity. The parameters of the governing equation are dynamically controlled using exponential functions. The overall methodology effectively regulates the velocities of particles during their whole course of flight in such a way that results in substantial improvement of the performance of PSO. The effectiveness of the proposed method has been investigated on multi-area 4 generators, 40 generators and 140 generators test systems with multiple constraints such as reserve sharing and tie-line power. The application results show that the proposed DCPSO method is very promising for large-dimensional MAED problems.

The Auxiliary Problem Principle with Self-Adaptive Penalty Parameter for Multi-Area Economic Dispatch Problem

The auxiliary problem principle is a powerful tool for solving multi-area economic dispatch problem. One of the main drawbacks of the auxiliary problem principle method is that the convergence performance depends on the selection of penalty parameter. In this paper, we propose a self-adaptive strategy to adjust penalty parameter based on the iterative information, the proposed approach is verified by two given test systems. The corresponding simulation results demonstrate that the proposed self-adaptive auxiliary problem principle iterative scheme is robust in terms of the selection of penalty parameter and has better convergence rate compared with the traditional auxiliary problem principle method.

The Engineering Application of Improved Alternating Direction Method for Solving Power Systems Multi-Area Dispatch Problem

The alternating direction method has been widespread used to solve multi-area economic dispatch problem. Compared with traditional centered economic dispatch, alternating direction method divides centered optimal problem into completely independent sub-problems while the corresponding equality and inequality constraints are satisfied. However, plenty of applications show that the choice of penalty parameter for the consistency constraint has an important influence on the convergence performance of alternating direction method. In this paper, we proposed a novel improved alternating direction method. To be more exact, the key is to adjust penalty parameter based on iterative information of alternating direction method. Numerical results illustrate the proposed method has better stability in convergence.

Improved Particle Swarm Optimization for Multi-area Economic Dispatch with Reserve Sharing Scheme

This paper presents an improved particle swarm optimization (IPSO) to solve Multi-area Economic Dispatch (MAED) problem. The objective of MAED problem is to determine the optimal generating schedule of thermal units and inter-area power transactions in such a way that total fuel cost is optimized while satisfying tie-line, spinning reserve and other operational constraints. The spinning reserve requirements for reserve sharing provisions are investigated by considering contingency and pooling spinning reserves. The control equation of IPSO is modified by suggesting improved cognitive component of the particle’s velocity by suggesting preceding experience. The operators of IPSO are also modified to maintain a proper balance between cognitive and social behavior of the swarm. The effectiveness of the proposed method has been tested on four areas, 16 generators and 40 generators test systems. The application results show that IPSO is very promising to solve MAED problem.

Artificial Immune System for Multi-Area Economic Dispatch

Abstract This article presents artificial immune system for solving multi-area economic dispatch (MAED) problem with tie line constraints considering transmission losses, multiple fuels, valve-point loading and prohibited operating zones. Artificial immune system is based on the clonal selection principle which implements adaptive cloning, hyper mutation, aging operator and tournament selection. The effectiveness of the proposed algorithm has been verified on three different test systems, both small and large, involving varying degree of complexity. Compared with differential evolution, evolutionary programming and real-coded genetic algorithm, considering the quality of the solution obtained, the proposed algorithm seems to be a promising alternative approach for solving the MAED problems in practical power system.

Hybrid Particle Swarm Optimization for Solving Multi-Area Economic Dispatch Problem

Hybrid Particle Swarm Optimization for Solving Multi-Area Economic Dispatch Problem

Hybrid Particle Swarm Optimization for Solving Multi-Area Economic Dispatch Problem

We consider the Multi-Area Economic Dispatch problem (MAEDP) in deregulated power system environment for practical multi-area cases with tie line constraints. Our objective is to generate allocation to the power generators in such a manner that the total fuel cost is minimized while all operating constraints are satisfied. This problem is NP-hard. In this paper, we propose Hybrid Particle Swarm Optimization (HGAPSO) to solve MAEDP. The experimental results are reported to show the efficiency of proposed algorithms compared to Particle Swarm Optimization with Time-Varying Acceleration Coefficients (PSO-TVAC) and RCGA.

Artificial bee colony optimization for multi-area economic dispatch

This paper presents artificial bee colony optimization for solving multi-area economic dispatch (MAED) problem with tie line constraints considering transmission losses, multiple fuels, valve-point loading and prohibited operating zones. Artificial bee colony optimization is a swarm-based algorithm inspired by the food foraging behavior of honey bees. The effectiveness of the proposed algorithm has been verified on three different test systems, both small and large, involving varying degree of complexity. Compared with differential evolution, evolutionary programming and real coded genetic algorithm, considering the quality of the solution obtained, the proposed algorithm seems to be a promising alternative approach for solving the MAED problems in practical power system.

A Novel EP Approach for Multi-area Economic Dispatch with Multiple Fuel Options

This paper presents a novel approach to multi-area economic dispatch problems with multiple fuel options using a hybrid evolutionary programming method. The objective is to minimize the operation cost of the entire system while satisfying the tie line constraints. In this paper, EP-LMO (Evolutionary Programming with Levenberg-Marquardt Optimization) technique is proposed to solve multi-area economic dispatch problems with multiple fuel options. The EP-LMO is developed in such a way that a simple evolutionary programming (EP) is applied as a base level search to find the direction of the optimal global region. And Levenberg-Marquardt Optimization (LMO) method is used as a fine tuning to determine the optimal solution. The applicability and validity of the proposed approach on multi-area economic dispatch problems are presented in two parts. In Part I, two multi-area bench mark problems without fuel options are considered. In Part II, 10 unit system with both multi-area and multi-fuel options is considered. The proposed approach is compared with the results of Incremental Network Flow Programming, Spatial Dynamic Programming and Evolutionary Programming approaches. The results show that the EP-LMO gives the optimum generation cost than any other methods.